Technology
Model Predictive Control

Cybernetica delivers Nonlinear Model Predictive Control (NMPC) applications based on mechanistic models. These applications provide better profitability, more stable product quality and safer operation than other technologies, particularly for processes that are demanding to control.

Cybernetica CENIT – Nonlinear Model Predictive Control

Model Predictive control is an advanced control method where a mathematical model of the process is used to predict future behavior. The predictions from the model are used in a mathematical optimization algorithm that calculates the optimal process inputs in order to achieve optimal future behavior of selected variables in the process. Constraints and setpoints may be imposed both on the manipulated process inputs variables and the controlled process output variables. Model predictive control also has the advantage that couplings between variables in the process are taken into account.Cybernetica CENIT is a powerful and versatile software suite for nonlinear model predictive control. It uses nonlinear mechanistic models which makes it better and more robust than alternative technologies that often rely on linear models. CENIT is well suited for control of many demanding processes, including:

Processes that are highly nonlinear, requiring nonlinear models.

Processes where important variables to be controlled cannot be measured on-line but have to be estimated.

The models used in CENIT are developed specifically for nonlinear model predictive control. Even though the model captures nonlinear dynamics of the process, there will always be uncertainty in a mathematical process model. The mechanistic structure of the models allows for very efficient compensation of this deviation in CENIT’s estimator algorithms. The combination of mechanistic models with on-line model adaption are crucial elements in the successful application of NMPC technology. Our approach allows for development of process models using data from regular operation in order to minimize impact on production. Cybernetia CENIT will control your plant in an optimal way, and you can benefit from improved productivity, improved consistency of product quality, reduced emissions, reduced energy consumption, and improved safety.

Model and application development

Process models as well as application specific codes for control of the estimator and controller algorithms are implemented in a Cybernetica Model and Application Component, which is linked into the CENIT system. This separation allows for very specific tailoring in order to best meet our customer needs, while we still build the total application upon a general software kernel and a collection of advanced algorithms for nonlinear estimation and control. Cybernetica Model and Application Component features:

Safe operation

The CENIT system will ensure that the process is safely controlled in spite of uncertain and changing process characteristics. The estimator algorithms in CENIT will continuously adjust the model to compensate for any deviations between model predictions and the observed process behavior. In addition to the inherent safety associated with the MPC methodology and the online model adaptation, the CENIT system has a number of built-in safety features:

Internal fail checking in CENIT.

CENIT monitors the health of all measurements and can handle faults in instrumentation by taking automatic protective action or alarming the operator.

Diagnostic information is provided to the DCS and to the operators.

Offline development tools

Development of the Cybernetica Model and Application Component is a critical activity in every CENIT implementation project. Cybernetica has several tools to aid the control engineer and to facilitate efficient workflow for developement and maintenance of CENIT installations.

Cybernetica ModelFit – Offline estimation and model validation

Cybernetica ModelFit is a tool used for off-line estimation of model states and parameters, for model validation, and for design of the on-line estimation part of Cybernetica CENIT applications. ModelFit is used to decide which model parameters should be estimated on-line, to design the on-line estimators, and to estimate the parameters that are considered constant. ModelFit interfaces to Cybernetica Model and Application Components, and it supports the same model formats as CENIT. The features include:

Design and tuning of on-line estimators in CENIT applications.

Estimation of constant or time varying model parameters.

Estimation of initial states.

Simultaneous use of multiple data sets.

Parameter identifiability analysis.

Cybernetica ModelFit is flexible with respect to configuration of the parameter estimation. Parameters can be time varying or constant. Multiple data sets from different operating conditions may be used to find the best parameter fit taken all data sets into account.

Cybernetica RealSim – Simulation of closed loop system

Cybernetica RealSim is a plant replacement process simulator used for testing of CENIT or other control applications. It communicates over the OPC protocol in order to replicate the interface to the DCS at the plant as closely as possible. It interfaces to Cybernetica Model and Application Components. The plant replacement model might be the same as the model used in CENIT or it might be a different one in order to evaluate how the controller responds to model uncertainty and unknown process disturbances. Cybernetica RealSim is typically used during application development and for factory acceptance tests (FAT).

Questions? Contact me!

Tor Steinar Schei, Dr.ing.

Technical Director and Chairman of the Board

+47 93 05 93 14

[obfuscated email address]

Cybernetica CENIT features:

Nonlinear Model Predictive Control

Divided Difference Kalman Filtering

Moving Horizon Estimation

Process models:

First principles nonlinear models

Support for Modelica models

Supports integration via Functional Mockup Interface standard

Runs on Microsoft Windows

Can be delivered for embedded systems

OPC communication

History collection database

Typical applications:

Control of nonlinear processes operated under varying conditions

Optimal product grade transitions

Control of batch and semi-batch processes

Control of unmeasured process variables such as conversion rates and product quality